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    Coursera
    1. Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
    2. Week 3
    3. Hyperparameter tuning, Batch Normalization, Programming Frameworks
    PreviousNext
      • Completed
        Video: LectureTuning Process
        . Duration: 7 minutes7 min
      • Completed
        Video: LectureUsing an Appropriate Scale to pick Hyperparameters
        . Duration: 8 minutes8 min
      • Completed
        Video: LectureHyperparameters Tuning in Practice: Pandas vs. Caviar
        . Duration: 6 minutes6 min
      • Completed
        Video: LectureNormalizing Activations in a Network
        . Duration: 8 minutes8 min
      • Completed
        Video: LectureFitting Batch Norm into a Neural Network
        . Duration: 12 minutes12 min
      • Completed
        Video: LectureWhy does Batch Norm work?
        . Duration: 11 minutes11 min
      • Completed
        Video: LectureBatch Norm at Test Time
        . Duration: 5 minutes5 min
      • Completed
        Quiz: Hyperparameter tuning, Batch Normalization, Programming Frameworks
        10 questions
      • Completed
        Programming Assignment: TensorFlow Introduction
        . Duration: 3 hours3h
      • Completed
        Reading: References
        . Duration: 10 minutes10 min
      • Completed
        Reading: Acknowledgments
        . Duration: 10 minutes10 min
    QuizQuiz • 20 min20 minutes

    Hyperparameter tuning, Batch Normalization, Programming Frameworks

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    Hyperparameter tuning, Batch Normalization, Programming Frameworks
    Graded Quiz • 20 min

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    Hyperparameter tuning, Batch Normalization, Programming Frameworks

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    1.
    Question 1

    If searching among a large number of hyperparameters, you should try values in a grid rather than random values, so that you can carry out the search more systematically and not rely on chance. True or False?

    1 / 1 point
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    2.
    Question 2

    Every hyperparameter, if set poorly, can have a huge negative impact on training, and so all hyperparameters are about equally important to tune well. True or False?

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    Yes. We've seen in lecture that some hyperparameters, such as the learning rate, are more critical than others.

    3.
    Question 3

    During hyperparameter search, whether you try to babysit one model (“Panda” strategy) or train a lot of models in parallel (“Caviar”) is largely determined by:

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    4.
    Question 4

    If you think β\betaβ (hyperparameter for momentum) is between 0.9 and 0.99, which of the following is the recommended way to sample a value for beta?

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    5.
    Question 5

    Finding good hyperparameter values is very time-consuming. So typically you should do it once at the start of the project, and try to find very good hyperparameters so that you don’t ever have to revisit tuning them again. True or false?

    1 / 1 point
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    6.
    Question 6

    In batch normalization as presented in the videos, if you apply it on the lllth layer of your neural network, what are you normalizing?

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    7.
    Question 7

    In the normalization formula znorm(i)=z(i)−μσ2+εz_{norm}^{(i)} = \frac{z^{(i)} - \mu}{\sqrt{\sigma^2 + \varepsilon}}znorm(i)​=σ2+ε​z(i)−μ​, why do we use epsilon?

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    8.
    Question 8

    Which of the following statements about γ\gammaγ and β\betaβ in Batch Norm are true?

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    9.
    Question 9

    After training a neural network with Batch Norm, at test time, to evaluate the neural network on a new example you should:

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    10.
    Question 10

    Which of these statements about deep learning programming frameworks are true? (Check all that apply)

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